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the molecular level. While structural predictions using deep learning methods like AlphaFold have revolutionized our understanding of sequence dependent molecular structure, we currently have much more limited
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biofilm-related infections in orthopaedic implants: from in vitro to in vivo models Join the prestigious Marie Skłodowska-Curie Actions Doctoral Network SHIELD (Strategies for Healing Implant-associated
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flow, fluid dynamics, and sustainable energy systems. The research focuses on developing new methods to study and model multiphase flows as key phenomena in energy and industrial processes. The work
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responsibility is to conduct high-quality research on hybrid artificial intelligence. You will: Combine deep learning to capture long-term patterns and uncertainties with stochastic model predictive control
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twins, semantic modeling, secure data exchange, and reconfigurable production architectures. The research will also be carried out in LTU’s AIC³ Lab, an advanced test environment for future industrial
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intelligent, adaptive, and sustainable industrial systems. The work involves topics such as AI-assisted automation engineering, digital twins, semantic modeling, secure data exchange, and reconfigurable
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. With cutting-edge research, top-tier education, and extensive collaborations, we are a key force in the field. Our core competencies include in vitro ADME models, advanced in vivo methods, computational
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through a combination of registry-based epidemiological studies, in vitro modelling of biofilm behaviour, and in vivo investigations of antibiotic pharmacokinetics and efficacy. The project is part of
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collaborations, we are a key force in the field. Our core competencies include in vitro ADME models, advanced in vivo methods, computational modeling and simulation, as well as patient-focused and policy-related
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, Internet of Things, Systems-of-Systems automation, Machine Learning, Deep Learning, Data Science, Electronic systems design, and sensor systems. Cyber-Physical Systems (CPS) focuses on integrated software